farabiDesigned system · AI · Blockchain · Quantum communications
Quantum AI Communications
An AI-enabled, blockchain-based distributed quantum communication system using machine learning and generative AI to secure, monitor and coordinate long-range transmission.
System designed and sold to a startup · White-label delivery
Interactive system explanation / Q-Chain Nexus
How intelligence,
trust and quantum links connect.
What it means / AI + policy control
AI + policy control
Machine learning anticipates link problems. Generative AI explains what is happening and recommends a bounded response. Fixed safety rules make the final decision.
A designed system, not a theory
Quantum AI Communications was designed as an AI-enabled, blockchain-based distributed quantum communication system and sold to a startup as a white-label technology asset. The work defined the operating architecture, security boundaries, control logic, simulator and delivery path for secure long-range transmission.
The system is deliberately hybrid: post-quantum cryptography protects every authorised session, while quantum key distribution can strengthen the key path when measured link conditions permit it. Fibre, free-space and satellite routes form part of the network model without claiming that a blockchain transmits qubits or replaces quantum hardware.
The system in plain language
Two authorised endpoints connect through secure gateways. Their encrypted messages travel off-chain, while a protected key broker chooses either a QKD-assisted route or a post-quantum fallback. Applications receive a stable secure session without needing to understand which path is active.
A permissioned blockchain coordinates trusted identities, route and policy versions, and tamper-evident delivery evidence. It stores no messages, secret keys or quantum key material. That separation keeps the ledger useful for assurance without turning it into a privacy or performance bottleneck.
Machine learning and generative AI
Machine learning is used to estimate route reliability, detect degradation and anticipate key-pool exhaustion from measured telemetry. Generative AI acts as an explainable operations copilot: it summarises network state, predicts likely service impact, recommends bounded actions and translates an approved objective into a typed policy proposal.
AI never generates keys, chooses an unapproved cryptographic algorithm or changes security thresholds by itself. Every proposal passes through a deterministic policy engine that enforces the post-quantum security floor, expiry, authorisation, route health and separation of duties.
A hybrid quantum and post-quantum security path
The deployable baseline establishes sessions with ML-KEM-768 and authenticated symmetric encryption such as AES-256-GCM. When QBER, secret-key rate, key-pool depth and link health meet policy thresholds, QKD-derived material is combined with the independently authenticated post-quantum exchange.
If the quantum route degrades, the broker does not lower the security floor. It moves the session to an ML-KEM fallback, can propose a reroute and records non-sensitive evidence of the decision. The same architecture can span fibre, protected trusted nodes and intermittent satellite or free-space links.
Blockchain as coordination and evidence
The permissioned ledger provides Byzantine-fault-tolerant finality for endpoint identities, certificate and policy versions, route identifiers, telemetry commitments, session lifecycle events, AI recommendation IDs and approval decisions.
Plaintext, private keys, QKD keys and raw sensitive telemetry remain outside the chain. Post-quantum signatures protect validator and application records so the blockchain layer supports audit and governance rather than becoming the data-transport layer.
Simulator and delivery record
The Q-Chain control-plane simulator models the decisions around two measurable states. A healthy link with 1.2% QBER, an 80,000 bps secret-key rate and a one-million-bit pool selects the QKD-plus-ML-KEM hybrid. A degraded link with 8.1% QBER, a 2,000 bps rate and a 128-bit pool triggers the ML-KEM fallback and a reroute recommendation.
The simulator is a control and policy demonstrator rather than a cryptographic implementation. It proves the decision boundaries, metadata-only ledger commitments and AI safety gates that were designed into the wider system, while keeping cryptographic deployment dependent on approved hardware and standards-compliant components.